Biometric methods can be applied to authenticate users; that is to say, the user is recognized by bodily features or characteristic modes of behavior. Multimodal biometry integrates two or more individual biometries (for example, speaker, signature, hand geometry, fingerprint, iris, face recognition) into an overall system. Biometric methods with individual biometries are disclosed, for example, in the form of dynamic signature verifications in WO 98/24051, WO 98/25228, WO 98/50880, WO 99/08223, in the form of a speaker verification in DE 19952049 A1, and as methods for hand recognition or for determining the position of a hand in U.S. Pat. Nos. 5,533,177, 5,751,843, 5,828,779, EP 0 560 779 B1, EP 0 713 592 B1, EP 0 800 145 A2 and WO 98/38533.
Multiple biometries can contribute to higher security and/or to enhanced comfort. The assignments for the individual biometries must be optimally combined or fused in this case.
The following requirements can be demanded in detail of a multimodal biometric system:                higher security than best individual biometry;        quick assignment time;        low and, in particular, prescribed average false acceptance rates FAR; and        low and, in particular, prescribed average false rejection rate FRR.        
Biometric methods are two-class assignment problems in which the features of the class of the originals (authorized users) are to be separated optimally from the features of the imposter class. Multimodal biometrics also constitute binary classification problems, the dimension of the feature space being a function of the number of the individual biometries used.
Various approaches exist for combining a number of individual biometries in a multimodal biometric method. These are:                Logic operation: AND-/OR-/combined operation; after Dieckmann, U. et al. “SESAM: A biometric person identification system using sensor fusion”, Pattern Recognition Letters 18, 1997, pages 827–833.        Weighted total score: from individual scores or individual costs, for overall threshold; after Brunelli, R. und Falavigna, D. “Person Identification Using Multiple Cues”, IEEE. Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 10, 1995.        Support vector machine (SEM): support vectors that separate the classes are determined by training process; after Ben-Yacoub, S. “Multi-Modal Data Fusion For Person Authentication Using SVM”, IDIAP Research Report 98-07 Martigny-Valais-Suisse, 1998.        Bayesian statistics: normal distributions of the scores are presupposed; after Bigün E. S. et al. “Expert Conciliation for Multi Modal Person Authentication Systems by Bayesian Statistics”, Proceedings 1st Int. Conf. On Audio-& Video-Based Personal Authentication, 1997, pages 327–334.        Neural networks (NN): data-driven class separation; after Brunelli, R. und Falavigna, D. “Person Identification Using Multiple Cues”, IEEE. Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 10, 1995.        
In all biometric methods, reference and test features are compared with one another, and a decision is made with the aid of similarity measures as to whether they originate from the same user. In other words, the similarity measures are measures of the similarity (correspondence) of biometric data determined for the user to reference data of users for the respective class. Measures of the similarity are either distances, so-called costs, between reference and test features or patterns, or so-called scores that constitute a measure of the probability that reference and test features originate from the same user. The value range of the costs lies between zero and a certain maximum value, low costs corresponding to high similarity, and high costs to low similarity. The values of the scores lie in the range between zero and one. Scores of one stand for maximum correspondence, and scores of zero for minimum correspondence.
With reference to the prior art, the combination of biometries via logic operations, which is chiefly used in the case of current commercial multimodal systems, may be explained by way of example. An AND operation is the logic combination of n biometries, a user being accepted only when for all individual biometries the costs (K1, K2, . . , Kn) lie below, or the scores (S1, S2, . . , Sn) lie above, specific thresholds (T1, T2, . . , Tn).
Logic AND operation criterion in the case of costs:(K1<T1)&(K2<T2)& . . . &(Kn<Tn)
Logic AND operation criterion in the case of scores:(S1>T1)&(S2>T2)& . . . &(Sn>Tn)
A possible assignment limit G and the associated acceptance region A (hatched region) of the costs and scores in the case of a logic AND operation for the combination of two biometrics 1 and 2 explained more accurately further below are illustrated in FIG. 1. The costs K1 of the biometry 1 are plotted on the abscissa, and the scores S2 of the biometry 2 are plotted on the ordinate.
In the case of the OR combination, in a biometry a user need generate only costs below, or scores above, a specific threshold value.
Logic OR operation criterion in the case of costs:(K1<T1)|(K2<T2)| . . . |(Kn<Tn)
Logic OR operation criterion in the case of scores:(S1>T1)|(S2>T2)| . . . |(Sn>Tn)
An assignment limit G and the associated acceptance region A (hatched region) of the costs or scores in the case of a logic OR operation for the combination of biometry 1 and biometry 2 are shown, by way of example, in FIG. 2.
The existing approaches and systems partly exhibit a few disadvantages. These are concrete assumptions relating to the individual biometrics, for example, that costs of the individual biometries are normally distributed, a high training outlay (NN, SVM), inadequate error rates and difficult modification for the use of other biometries, systems or another number of biometries, since the combinations such as, for example, weights in the case of the methods of weighted total scores are optimized to the systems and biometries used and to their number.
It is an object of the present invention to develop a multimodal biometry in the case of which the disadvantages outlined are avoided, and in the case of which, in particular, it is possible to set or select a desired false acceptance rate and, therefore, a certain degree of security.